




'''simple docstring'''







from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block

@dataclass
class 	_snake_case(			UpperCAmelCase      ):
 __snake_case:							torch.FloatTensor




class 	_snake_case(			UpperCAmelCase       , UpperCAmelCase      ):






 @register_to_config
 def __init__(self       :  str    ,		a       :  int = 6_55_36    ,		a       :  Optional[int] = None    ,		a       :  int = 2    ,		a       :  int = 2    ,		a       :  int = 0    ,		a       :  str = "fourier"    ,		a       :  bool = True    ,		a       :  bool = False    ,		a       :  float = 0.0    ,		a       :  Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")    ,		a       :  Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")    ,		a       :  Tuple[str] = "UNetMidBlock1D"    ,		a       :  str = None    ,		a       :  Tuple[int] = (32, 32, 64)    ,		a       :  str = None    ,		a       :  int = 8    ,		a       :  int = 1    ,		a       :  bool = False    ,		)       ->   str:






    """simple docstring"""


    super().__init__()
    A__       =     sample_size

    # time
    if time_embedding_type == "fourier":
       A__       =     GaussianFourierProjection(
           embedding_size=8    ,		set_W_to_weight=a    ,		log=a    ,		flip_sin_to_cos=a    )
       A__       =     2 * block_out_channels[0]
    elif time_embedding_type == "positional":
       A__       =     Timesteps(
           block_out_channels[0]    ,		flip_sin_to_cos=a    ,		downscale_freq_shift=a    )
       A__       =     block_out_channels[0]

    if use_timestep_embedding:
       A__       =     block_out_channels[0] * 4
       A__       =     TimestepEmbedding(
           in_channels=a    ,		time_embed_dim=a    ,		act_fn=a    ,		out_dim=block_out_channels[0]    ,		)

    A__       =     nn.ModuleList([]    )
    A__       =     None
    A__       =     nn.ModuleList([]    )
    A__       =     None

    # down
    A__       =     in_channels
    for i, down_block_type in enumerate(a    ):
       A__       =     output_channel
       A__       =     block_out_channels[i]

       if i == 0:
          input_channel += extra_in_channels

       A__       =     i == len(a    ) - 1

       A__       =     get_down_block(
           a    ,		num_layers=a    ,		in_channels=a    ,		out_channels=a    ,		temb_channels=block_out_channels[0]    ,		add_downsample=not is_final_block or downsample_each_block    ,		)
       self.down_blocks.append(a    )

    # mid
    A__       =     get_mid_block(
        a    ,		in_channels=block_out_channels[-1]    ,		mid_channels=block_out_channels[-1]    ,		out_channels=block_out_channels[-1]    ,		embed_dim=block_out_channels[0]    ,		num_layers=a    ,		add_downsample=a    ,		)

    # up
    A__       =     list(reversed(a    )    )
    A__       =     reversed_block_out_channels[0]
    if out_block_type is None:
       A__       =     out_channels
    else:
       A__       =     block_out_channels[0]

    for i, up_block_type in enumerate(a    ):
       A__       =     output_channel
       A__       =     (
           reversed_block_out_channels[i + 1] if i < len(a    ) - 1 else final_upsample_channels
       )

       A__       =     i == len(a    ) - 1

       A__       =     get_up_block(
           a    ,		num_layers=a    ,		in_channels=a    ,		out_channels=a    ,		temb_channels=block_out_channels[0]    ,		add_upsample=not is_final_block    ,		)
       self.up_blocks.append(a    )
       A__       =     output_channel

    # out
    A__       =     norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4    ,		32    )
    A__       =     get_out_block(
        out_block_type=a    ,		num_groups_out=a    ,		embed_dim=block_out_channels[0]    ,		out_channels=a    ,		act_fn=a    ,		fc_dim=block_out_channels[-1] // 4    ,		)






 def 						_UpperCamelCase (self       :  List[str]    ,		a       :  torch.FloatTensor    ,		a       :  Union[torch.Tensor, float, int]    ,		a       :  bool = True    ,		)       ->   Union[UNetaDOutput, Tuple]:






    """simple docstring"""


    A__       =     timestep
    if not torch.is_tensor(a    ):
       A__       =     torch.tensor([timesteps]    ,		dtype=torch.long    ,		device=sample.device    )
    elif torch.is_tensor(a    ) and len(timesteps.shape    ) == 0:
       A__       =     timesteps[None].to(sample.device    )

    A__       =     self.time_proj(a    )
    if self.config.use_timestep_embedding:
       A__       =     self.time_mlp(a    )
    else:
       A__       =     timestep_embed[..., None]
       A__       =     timestep_embed.repeat([1, 1, sample.shape[2]]    ).to(sample.dtype    )
       A__       =     timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])    )

    # 2. down
    A__       =     ()
    for downsample_block in self.down_blocks:
       A__  ,			A__       =     downsample_block(hidden_states=a    ,		temb=a    )
       down_block_res_samples += res_samples

    # 3. mid
    if self.mid_block:
       A__       =     self.mid_block(a    ,		a    )

    # 4. up
    for i, upsample_block in enumerate(self.up_blocks    ):
       A__       =     down_block_res_samples[-1:]
       A__       =     down_block_res_samples[:-1]
       A__       =     upsample_block(a    ,		res_hidden_states_tuple=a    ,		temb=a    )

    # 5. post-process
    if self.out_block:
       A__       =     self.out_block(a    ,		a    )

    if not return_dict:
       return (sample,)

    return UNetaDOutput(sample=a    )








'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase_     						=				{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_     						=				[
        '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''RobertaPreLayerNormForCausalLM''',
        '''RobertaPreLayerNormForMaskedLM''',
        '''RobertaPreLayerNormForMultipleChoice''',
        '''RobertaPreLayerNormForQuestionAnswering''',
        '''RobertaPreLayerNormForSequenceClassification''',
        '''RobertaPreLayerNormForTokenClassification''',
        '''RobertaPreLayerNormModel''',
        '''RobertaPreLayerNormPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_     						=				[
        '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFRobertaPreLayerNormForCausalLM''',
        '''TFRobertaPreLayerNormForMaskedLM''',
        '''TFRobertaPreLayerNormForMultipleChoice''',
        '''TFRobertaPreLayerNormForQuestionAnswering''',
        '''TFRobertaPreLayerNormForSequenceClassification''',
        '''TFRobertaPreLayerNormForTokenClassification''',
        '''TFRobertaPreLayerNormMainLayer''',
        '''TFRobertaPreLayerNormModel''',
        '''TFRobertaPreLayerNormPreTrainedModel''',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_     						=				[
        '''FlaxRobertaPreLayerNormForCausalLM''',
        '''FlaxRobertaPreLayerNormForMaskedLM''',
        '''FlaxRobertaPreLayerNormForMultipleChoice''',
        '''FlaxRobertaPreLayerNormForQuestionAnswering''',
        '''FlaxRobertaPreLayerNormForSequenceClassification''',
        '''FlaxRobertaPreLayerNormForTokenClassification''',
        '''FlaxRobertaPreLayerNormModel''',
        '''FlaxRobertaPreLayerNormPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
        RobertaPreLayerNormConfig,
        RobertaPreLayerNormOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_roberta_prelayernorm import (
            ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            RobertaPreLayerNormForCausalLM,
            RobertaPreLayerNormForMaskedLM,
            RobertaPreLayerNormForMultipleChoice,
            RobertaPreLayerNormForQuestionAnswering,
            RobertaPreLayerNormForSequenceClassification,
            RobertaPreLayerNormForTokenClassification,
            RobertaPreLayerNormModel,
            RobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_roberta_prelayernorm import (
            TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRobertaPreLayerNormForCausalLM,
            TFRobertaPreLayerNormForMaskedLM,
            TFRobertaPreLayerNormForMultipleChoice,
            TFRobertaPreLayerNormForQuestionAnswering,
            TFRobertaPreLayerNormForSequenceClassification,
            TFRobertaPreLayerNormForTokenClassification,
            TFRobertaPreLayerNormMainLayer,
            TFRobertaPreLayerNormModel,
            TFRobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_roberta_prelayernorm import (
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase_     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


